The promise of internationally collaborative research for studying occupation: the example of the older women's food preparation study

Growing awareness of the Western perspectives underpinning occupational science and occupational therapy’s values, theories, and evaluation tools has given rise to questions about culturally relevant knowledge and practice with non-Western populations. To make sense of attempts to develop cross-cultural knowledge taking place within the profession and discipline, the authors review epistemological perspectives and methodological advances in anthropology and psychology. Thus informed, they both summarize and critique constructivist and positivist approaches to knowledge development and practice that cross or resist the crossing of cultures. The authors outline a multicultural collaborative research method that supports extending and refining the profession’s knowledge in a way that both honors local perspectives and reveals concepts that cross cultures. Insights from a study that explored the meaning of food preparation to older Thai, American, and New Zealand women provide illustrative examples

Fair scans of the seesaw. Consequences for predictions on LFV processes

Usual analyses based on scans of the seesaw parameter-space can be biassed since they do not cover in a fair way the complete parameter-space. More precisely, we show that in the common "R-parametrization", many acceptable R-matrices, compatible with the perturbativity of Yukawa couplings, are normally disregarded from the beginning, which produces biasses in the results. We give a straightforward procedure to scan the space of complex R-matrices in a complete way, giving a very simple rule to incorporate the perturbativity requirement as a condition for the entries of the R-matrix, something not considered before. As a relevant application of this, we show that the extended believe that BR(mu --> e, gamma) in supersymmetric seesaw models depends strongly on the value of theta_13 is an "optical effect" produced by such biassed scans, and does not hold after a careful analytical and numerical study. When the complete scan is done, BR(mu --> e, gamma) gets very insensitive to theta_13. Moreover, the values of the branching ratio are typically larger than those quoted in the literature, due to the large number of acceptable points in the parameter-space which were not considered before. Including (unflavoured) leptogenesis does not introduce any further dependence on theta_13, although decreases the typical value of BR(mu --> e, gamma).Comment: 22 pages, 5 figure

An empirical investigation of dance addiction

Although recreational dancing is associated with increased physical and psychological well-being, little is known about the harmful effects of excessive dancing. The aim of the present study was to explore the psychopathological factors associated with dance addiction. The sample comprised 447 salsa and ballroom dancers (68% female, mean age: 32.8 years) who danced recreationally at least once a week. The Exercise Addiction Inventory (Terry, Szabo, & Griffiths, 2004) was adapted for dance (Dance Addiction Inventory, DAI). Motivation, general mental health (BSI-GSI, and Mental Health Continuum), borderline personality disorder, eating disorder symptoms, and dance motives were also assessed. Five latent classes were explored based on addiction symptoms with 11% of participants belonging to the most problematic class. DAI was positively associated with psychiatric distress, borderline personality and eating disorder symptoms. Hierarchical linear regression model indicated that Intensity (ß=0.22), borderline (ß=0.08), eating disorder (ß=0.11) symptoms, as well as Escapism (ß=0.47) and Mood Enhancement (ß=0.15) (as motivational factors) together explained 42% of DAI scores. Dance addiction as assessed with the Dance Addiction Inventory is associated with indicators of mild psychopathology and therefore warrants further research

LHC and lepton flavour violation phenomenology of a left-right extension of the MSSM

We study the phenomenology of a supersymmetric left-right model, assuming minimal supergravity boundary conditions. Both left-right and (B-L) symmetries are broken at an energy scale close to, but significantly below the GUT scale. Neutrino data is explained via a seesaw mechanism. We calculate the RGEs for superpotential and soft parameters complete at 2-loop order. At low energies lepton flavour violation (LFV) and small, but potentially measurable mass splittings in the charged scalar lepton sector appear, due to the RGE running. Different from the supersymmetric 'pure seesaw' models, both, LFV and slepton mass splittings, occur not only in the left- but also in the right slepton sector. Especially, ratios of LFV slepton decays, such as Br(${\tilde\tau}_R \to \mu \chi^0_1$)/Br(${\tilde\tau}_L \to \mu \chi^0_1$) are sensitive to the ratio of (B-L) and left-right symmetry breaking scales. Also the model predicts a polarization asymmetry of the outgoing positrons in the decay $\mu^+ \to e^+ \gamma$, A ~ [0,1], which differs from the pure seesaw 'prediction' A=1$. Observation of any of these signals allows to distinguish this model from any of the three standard, pure (mSugra) seesaw setups.Comment: 43 pages, 17 figure

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Spatial Models of Abundance and Habitat Preferences of Commerson’s and Peale’s Dolphin in Southern Patagonian Waters

Funding: This research was possible with the support of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Funding for travel to and accommodation for NAD in Aberdeen, Scotland was provided by CONICET and Cetacean Society International. The work of NAD was part of a postdoctoral fellowship funded by CONICET. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development

Retroperitoneal liposarcomas: The experience of a tertiary Asian center

10.1186/1477-7819-9-12World Journal of Surgical Oncology9

Mechanism of resistance to trastuzumab and molecular sensitization via ADCC activation by exogenous expression of HER2-extracellular domain in human cancer cells

Trastuzumab, a humanized antibody targeting HER2, exhibits remarkable therapeutic efficacy against HER2-positive breast and gastric cancers; however, acquired resistance presents a formidable obstacle to long-term tumor responses in the majority of patients. Here, we show the mechanism of resistance to trastuzumab in HER2-positive human cancer cells and explore the molecular sensitization by exogenous expression of HER2-extracellular domain (ECD) in HER2-negative or trastuzumab-resistant human cancer cells. We found that long-term exposure to trastuzumab induced resistance in HER2-positive cancer cells; HER2 expression was downregulated, and antibody-dependent cellular cytotoxicity (ADCC) activity was impaired. We next examined the hypothesis that trastuzumab-resistant cells could be re-sensitized by the transfer of non-functional HER2-ECD. Exogenous HER2-ECD expression induced by the stable transfection of a plasmid vector or infection with a replication-deficient adenovirus vector had no apparent effect on the signaling pathway, but strongly enhanced ADCC activity in low HER2-expressing or trastuzumab-resistant human cancer cells. Our data indicate that restoration of HER2-ECD expression sensitizes HER2-negative or HER2-downregulated human cancer cells to trastuzumab-mediated ADCC, an outcome that has important implications for the treatment of human cancers